ฝัง
- เผยแพร่เมื่อ 2 ก.ค. 2024
- Bayesian statistical methods are becoming more common, but there are not many resources to help beginners get started. People who know Python can use their programming skills to get a head start. In this tutorial, I introduce Bayesian methods using grid algorithms, which help develop understanding, and MCMC, which is a powerful algorithm for real-world problems.
Tutorial information may be found at www.scipy2019.scipy.org/tutor...
See the full SciPy 2019 playlist at • SciPy 2019: Scientific...
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great lecture! really appreciate the thorough introduction. Mr. Downey is super enthusiastic and its great!
Allen has been lecturing this topic for several years now on Scipy and I am very grateful that he's teaching the common public such important content.
Thank you very much Mr. Allen Downey and Scipy ,Enthought
This was amazing! The grid approach is great to understand the concepts ... i was stuck while learning Bayesian statistics because i was being directly presented to the MCMC approach, which made things look more complex ... now things clicked for me. Thanks!
Woww, underrated workshop. For me this workshop and Bayesian Inference is Just Counting, give me the necessary clarity as a beginner to start in this subject. Surprisingly they explain the problem in two different manners that complement each other.
Great tutorial! Would suggest that for future tutorials, you should have the attendees generate their own binomial pfm via spreadsheet. More than anything, doing all the steps manually really helped me gain understanding of the mechanical steps involved.
I like the passion.
At 1:42:20, Allen corrects the verbose state of the function, but this error still persists on the GitHub page - Allen, just in case you are hearing!
I wanted to know about the Euro problem, whether the assessment by Barry Light (p < 0.07) was right or wrong, based on the analysis done through the Bayesian estimation?
The point is that you can determine exact prob distribution via Bayes where as frequentist method only provides a point estimate.
There are typos in the import statements for empyrical _dist in notebook 2,3
In case you did not figure it out yet, empyrical_dist has now been replaced with empiricaldist package, that you can install through pip. The latest notebooks on the github page work with this newer package that Allen updated to.
1:31:18 "the disease"
d6+d6 is not working as shown it is returning 6 elements with the corresponding probabilities
d6+d6 will work only if you are using empyrical_dist package, which Allen used at the workshop. Now, the new notebooks rather use empiricaldist package (available on PyPI), which has d6.add_dist(d6).
notebooks github.com/AllenDowney/BayesMadeSimple